期刊
APPLIED SPECTROSCOPY
卷 67, 期 4, 页码 463-469出版社
SAGE PUBLICATIONS INC
DOI: 10.1366/12-06790
关键词
Leaf Fluorescence; Classification; Symptomatic And Asymptomatic Leaves; Fluorescence Sensor
资金
- Citrus Research and Development Foundation (CRDF)
- US Department of Agriculture- National Institute of Food and Agriculture (USDA-NIFA)
A handheld fluorescence sensor was tested as a sensing tool to identify Huanglongbing (HLB), a citrus disease, in both symptomatic and asymptomatic stages. Features such as yellow, red, and far-red fluorescence at UV, blue, green, and red excitations, and other fluorescence ratios were acquired from the healthy and HLB-infected leaves of different cultivars. The classification studies were performed with these features as well as selective fluorescence features. Results indicated that the bagged decision tree classifier yielded 97% classification accuracy in case of the healthy and symptomatic samples. Although the asymptomatic samples from the HLB-infected trees could not be classified based on polymerase chain reaction (PCR) analysis results, the Naive-Bayes classifier grouped most of the asymptomatic samples as HLB. We found that a few fluorescence features such as yellow fluorescence (UV), far-red fluorescence (UV), yellow to far red fluorescence (UV), simple fluorescence ratio (green), and yellow fluorescence (green) could result in classification accuracies similar to those of the entire dataset.
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